7) Air Quality Management: Intelligent Realtime UAV Monitoring Systems

We aim to build a low-cost solution based on a fly-by-air platform (using amateur drones) in order to help with the monitoring and assessment of air quality in many scenarios. Using the data from drones as an input, along with data from existing ground stations, this pilot project will make use of artificial intelligence (AI) and optimisation techniques to create appropriate air pollution models. Machine learning (ML) approaches will learn from captured air pollution data to enable better predictions or extrapolation for a new given task, in this case air pollution prediction, trend setting and monitoring. We will build realtime and low-complexity algorithms that take as an input the fly-by-air UAV system monitoring data from hotspots. Amalgamated optimisation and AI methods relying on deep neural network models will be proposed for realtime computing and monitoring. Advanced technologies will be proposed under data-driven AI system to provide better understanding the mechanisms responsible for air pollutions. Developing air quality models, assisted by ground and UAV air quality data, can help us monitor and manage air pollution in a cost efficient manner.